# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# --------------------------------------------------------
# Copyright (c) OpenMMLab. All rights reserved.
"""Modified from https://github.com/LikeLy-Journey/SegmenTron/blob/master/
segmentron/solver/loss.py (Apache-2.0 License)"""
import torch
import torch.nn as nn
import torch.nn.functional as F

from ..builder import LOSSES
from .utils import get_class_weight, weighted_loss


@weighted_loss
def dice_loss(pred,
              target,
              valid_mask,
              smooth=1,
              exponent=2,
              class_weight=None,
              ignore_index=255):
    assert pred.shape[0] == target.shape[0]
    total_loss = 0
    num_classes = pred.shape[1]
    for i in range(num_classes):
        if i != ignore_index:
            dice_loss = binary_dice_loss(
                pred[:, i],
                target[..., i],
                valid_mask=valid_mask,
                smooth=smooth,
                exponent=exponent)
            if class_weight is not None:
                dice_loss *= class_weight[i]
            total_loss += dice_loss
    return total_loss / num_classes


@weighted_loss
def binary_dice_loss(pred, target, valid_mask, smooth=1, exponent=2, **kwargs):
    assert pred.shape[0] == target.shape[0]
    pred = pred.reshape(pred.shape[0], -1)
    target = target.reshape(target.shape[0], -1)
    valid_mask = valid_mask.reshape(valid_mask.shape[0], -1)

    num = torch.sum(torch.mul(pred, target) * valid_mask, dim=1) * 2 + smooth
    den = torch.sum(pred.pow(exponent) + target.pow(exponent), dim=1) + smooth

    return 1 - num / den


@LOSSES.register_module()
class DiceLoss(nn.Module):
    """DiceLoss.

    This loss is proposed in `V-Net: Fully Convolutional Neural Networks for
    Volumetric Medical Image Segmentation <https://arxiv.org/abs/1606.04797>`_.

    Args:
        smooth (float): A float number to smooth loss, and avoid NaN error.
            Default: 1
        exponent (float): An float number to calculate denominator
            value: \\sum{x^exponent} + \\sum{y^exponent}. Default: 2.
        reduction (str, optional): The method used to reduce the loss. Options
            are "none", "mean" and "sum". This parameter only works when
            per_image is True. Default: 'mean'.
        class_weight (list[float] | str, optional): Weight of each class. If in
            str format, read them from a file. Defaults to None.
        loss_weight (float, optional): Weight of the loss. Default to 1.0.
        ignore_index (int | None): The label index to be ignored. Default: 255.
        loss_name (str, optional): Name of the loss item. If you want this loss
            item to be included into the backward graph, `loss_` must be the
            prefix of the name. Defaults to 'loss_dice'.
    """

    def __init__(self,
                 smooth=1,
                 exponent=2,
                 reduction='mean',
                 class_weight=None,
                 loss_weight=1.0,
                 ignore_index=255,
                 loss_name='loss_dice',
                 **kwargs):
        super(DiceLoss, self).__init__()
        self.smooth = smooth
        self.exponent = exponent
        self.reduction = reduction
        self.class_weight = get_class_weight(class_weight)
        self.loss_weight = loss_weight
        self.ignore_index = ignore_index
        self._loss_name = loss_name

    def forward(self,
                pred,
                target,
                avg_factor=None,
                reduction_override=None,
                **kwargs):
        assert reduction_override in (None, 'none', 'mean', 'sum')
        reduction = (
            reduction_override if reduction_override else self.reduction)
        if self.class_weight is not None:
            class_weight = pred.new_tensor(self.class_weight)
        else:
            class_weight = None

        pred = F.softmax(pred, dim=1)
        num_classes = pred.shape[1]
        one_hot_target = F.one_hot(
            torch.clamp(target.long(), 0, num_classes - 1),
            num_classes=num_classes)
        valid_mask = (target != self.ignore_index).long()

        loss = self.loss_weight * dice_loss(
            pred,
            one_hot_target,
            valid_mask=valid_mask,
            reduction=reduction,
            avg_factor=avg_factor,
            smooth=self.smooth,
            exponent=self.exponent,
            class_weight=class_weight,
            ignore_index=self.ignore_index)
        return loss

    @property
    def loss_name(self):
        """Loss Name.

        This function must be implemented and will return the name of this
        loss function. This name will be used to combine different loss items
        by simple sum operation. In addition, if you want this loss item to be
        included into the backward graph, `loss_` must be the prefix of the
        name.
        Returns:
            str: The name of this loss item.
        """
        return self._loss_name
